Objectives

By the end of this session, you should be able to:

Import raw data
Transform your data using the dplyr package, a member of the tidyverse
Create this plot…

Create this plot

Tidy up!

Login to Duke’s Docker-ized version of RStudio Server

Login to your instance by going to https://vm-manage.oit.duke.edu/containers and entering your NetID.
Click on Docker
Click on RStudio
When RStudio loads, restart the R session (Ctrl/Cmd+Shift+F10), clear the console (Ctrl/Cmd+L), and clear your workspace

Open your project

Is your project still open? If not, click on the project icon to load it. (Don’t create a new one.)

Download and open the template

Run the following code in your console. Change products to your preferred subfolder.

  download.file("https://tinyurl.com/y9ze2grp", 
                destfile = "products/lab-w04.Rmd")

Optional: Change your layout

Tools > Global Options

Download and import the data

Run the download code chunk to get the data, and then write a line of code in the loadCSV chunk to import the data to your environment and assign it to an object called dat.

Inspect dat

Load the tidyverse package and take a quick glimpse at the data. Try glimpse(), then turn dat into a tibble.

  dat <- as.tibble(dat)

5 key functions

function	purpose
`filter()`	Pick observations by their values
`arrange()`	Reorder the rows
`select()`	Pick variables by their names
`mutate()`	Create new variables
`summarise()`	Create summaries

(http://r4ds.had.co.nz/transform.html)

Filter

filter() lets you subset based on row values. Just give it a data frame and one or more conditions.

  filter(dat, c.age.r1 < 13) # younger than 13
  filter(dat, c.age.r1 >= 11 & c.age.r1 <= 13) # between 11 and 13
  filter(dat, c.female.r1 != 0) # exclude males coded as 0's
  filter(dat, c.female.r1 == 1) # include females coded as 1's
  filter(dat, c.age.r1 < 13, c.female.r1 == 1) # females younger than 13
  filter(dat, (c.female.r1 == 1 & c.age.r1 < 11) | # females younger than 11
              (c.female.r1 == 0 & c.age.r1 > 12)) # OR males older than 12

Logical operators and (&), or (|), not (!), equals (==). How many observations do we have of female caregivers (p.female.r1) and female children (c.female.r1)?

Arrange

arrange() sorts your dataframe according to one or more conditions.

  arrange(dat, p.female.r1, p.age.r1) # both ascending
  arrange(dat, p.female.r1, desc(p.age.r1)) # descending age

Try arranging by child age and then gender. Note how the order matters.

Select

select() lets you grab just a subset of columns from your data frame.

  select(dat, p.female.r1, c.female.r1)
  select(dat, starts_with("p.")) # grab all p. columns
  select(dat, ends_with(".r1")) # grab all .r1 columns
  select(dat, contains("age")) # grab all age columns

Get all of the “knows” variables.

Mutate

mutate() creates new columns. In this example, we first create a smaller data frame called temp to make it easier to see the new variable ageDifference that we create in the second step.

  temp <- select(dat, contains("age"), c.female.r1) # females + all age columns
  mutate(temp, ageDiff = p.age.r1 - c.age.r1, # parent age - kid age
               ageDiffLog = log(ageDiff), # log of age difference
               age50 = ifelse(p.age.r1 >=50, 1, 0)) # 1 if diff at least 50yrs

Add a step to mutate that creates a variable in temp called ageDiff50 that equals 1 if the age difference between kids and caregivers is at least 50 years.

Summarise

summarise() collapses the data to summary values. It can be used with the group_by function to summari(z)e by group.

  summarise(temp, c.ageM = mean(c.age.r1)) # mean of child age
  by_gender <- group_by(temp, c.female.r1) # group by child gender
  summarise(by_gender, c.ageM = mean(c.age.r1))

Pipes!

The Pipe

%>%

We can “pipe” together multiple steps without having to make intermediate objects like temp.

# meanAge <-   # optional to assign result to some object 
  dat %>% 
    select(contains("age"), c.female.r1) %>%
    mutate(ageDiff = p.age.r1 - c.age.r1) %>%
    group_by(c.female.r1) %>%
    summarise(c.ageM = mean(c.age.r1))

Create a pipe that calculates the mean age difference between parents and kids by parent gender.

Our Objective

Data dictionary

To make this plot, we need 4 sets of variables that assess what the child knows and when he/she learned

Construct new variables: Full disclosure

The first variable to construct is an indicator of whether we should classify the child as meeting criteria for full disclosure. We use the case_when() function and a few logical operators to create this new variable p.fullDisclosure.r1.

  temp2 <- 
  mutate(dat,
         p.fullDisclosure.r1 = case_when(
            doesKnow16.r1=="yes, child knows" &
            doesKnow20.r1=="yes, child knows" &
            doesKnow24.r1=="yes, child knows" ~ 1, 
            TRUE ~ 0
            )
         )
  temp2$p.fullDisclosure.r1

(We create temp2 just to make it easy to see the result.)

Construct new variables: Age at full disclosure

  temp2 <- 
  mutate(temp2,
         ageFull = pmax(doesKnow23.r1, doesKnow27.r1, na.rm=T)
         ) %>%
  mutate(ageFull = ifelse(p.fullDisclosure.r1==1, ageFull, NA)
  )
  temp2$ageFull